From Software Engineer to Data Scientist: One Udacity Grad’s Machine Learning-Powered Career Change

By Adam Lane August 11, 2018

After twenty years as a software engineer, this lifelong learner decided to enroll in Udacity’s Machine Learning Nanodegree program, and he now has an amazing new role as a Data Scientist!

Over the course of his twenty-year career in software development, Antal Berenyi has learned many important lessons. Perhaps the most important of these is the need to keep learning and redefining what you do. Antal just graduated from Udacity’s Machine Learning Nanodegree program, and he’s now applying the skills he learned in the program to a new data scientist role. He’s a passionate advocate for the positive impact machine learning can have on society, and he’s made it his mission to play a leading role in making this happen.

We spoke with Antal to learn how he acquired his new machine learning skills, and how he used his project portfolio to land his new role.

Becoming an advocate for machine learning

Antal’s excitement about machine learning is both well-timed and much-needed. While a wide swath of industries are already being impacted by this powerful technology, there are still many companies not yet taking advantage of what machine learning techniques offer. Fortunately, advocates like Antal are making a difference, by proactively learning new skills, and encouraging their employers to embrace machine learning-powered innovation.

Let’s start by hearing a little about your background. What were you doing before you started your Nanodegree program?

I have held software development roles in a range of different industries. Now I’m in biotech, where I worked for about eight years before starting the program. I have worked in many different areas—on things like automation software, and some traditional data analysis.

You were already an experienced tech professional. Can you tell us why you decided to enroll in the Machine Learning Nanodegree program?

I started reading more and more about machine learning, and became excited about all its different potential uses. Machine learning is really growing, and it’s going to be necessary in nearly every industry. I could see there were potential uses for it in my current workplace, for example. So there was a professional interest—I thought it would be useful to know about the techniques, tools, and tricks of the trade in machine learning so that I could use them in my job.

I’d also been working alongside a bio-statistician who was building predictive models. I was really excited about how machine learning could be used to find patterns, and make predictions about data that you couldn’t analyze by hand.

And there was also a personal interest—in simply wanting to understand more about machine learning. I think this personal motivation was especially important when it came to staying focused on learning something as complex as machine learning.

Accelerated learning with Udacity Connect

When Antal decided to take his interest in machine learning further, he knew the kind of learning experience he wanted. He enjoyed the flexibility of online learning, but he also craved interaction with fellow classmates in an actual classroom. As someone with a full-time job, he needed something efficient, and as a tech professional, he wanted something intensive. He found his ideal program when he enrolled in Udacity Connect, a blended learning experience combining online learning with on-site peer-to-peer collaboration and guided instruction. Connect students pursue aggressive timelines with the support of experienced sessions leads, and the engaged learning community serves to provide valuable accountability and motivation. This accelerative environment has powered remarkable results—on average, Connect students complete their programs up to five times faster than standard pacing.

What convinced you that Udacity Connect was the right learning experience for you?

I’m actually a really big fan of online learning. I love having all the materials—the videos, the exercises, everything—online, so you can study whenever you like. That’s really important. I didn’t think I could go back to sitting at a desk, learning in a more traditional style. But I’m also a people person, and I thought that the potential to connect with other students in the classroom would actually be really stimulating. And the program turned out to be exactly what I hoped! We exchanged ideas, we listened to speakers. I found being with other people really useful for accelerating my learning, particularly because machine learning was a new field for me.

The weekly cadence of in-person sessions was really useful for me. It gave us all time to do exercises, to qualify any issues we were having, to ask any questions. And it was time to catch up with the other students. Though perhaps ironically—because almost all the students had jobs and families—we actually still spent a lot of time catching up over the program’s online channels!

Can you tell us your thoughts on the inclusion of guest speakers as part of the program?

I really enjoyed hearing from the guest speakers that came in. That was really inspiring. It meant we could hear from real people working in the machine learning industry and find out what they were working on, and what they saw as the key industry trends.

“The projects really set the program apart from other learning experiences I’ve had. I think I got a lot out of them—in terms of new skills and experience working with different tools. They were also a great way for me to demonstrate what I learned when I made the switch to my new role as a Data Scientist with my company.”

New role, same company!

As many of our students do, Antal made an amazing switch to a new position within the company he already worked for. He identified a new opportunity to work on something that fascinated him, proactively learned new skills, then went to his employer and showed them he had the interest—and the abilities—to make a real impact in his chosen area.

When you’re thinking about your next career move, it’s important to remember that an internal move can be a great option. Moving to a new team in your company can provide fresh challenges and new reasons to get excited about coming into work each day. You can build new skillsets, develop different experiences, and set yourself up for future promotions and pay increases. As Antal found, learning new and in-demand skills with Udacity is a great way to make this kind of career move a reality.

How did your role as a Data Scientist come about?

I approached my boss and said I was interested in taking on projects that involved machine learning. I sent her a list of the courses I’d taken in the Nanodegree program, as well as some other data science courses. I think she was impressed with my eagerness and, combined with my previous experience, it didn’t take much convincing. I already had knowledge of the pharmaceutical domain, software development skills, and a familiarity with data. Then the Nanodegree program meant I added critical machine learning skills to my toolset.

I understand one of your Nanodegree program projects has carried over to your new role. Can you tell us about that?

Yes, for sure. I’m using a prediction model I worked on for a Udacity project in something I’m still working on now. And having those skills was really important.

“I found starting the new role to be a big undertaking—it was a big shift from my previous role—so feeling confident in my skills was important. And my company is quite small, so there aren’t many other people who work in the same area as me. It means I need to be able to rely on my own skills and experience to get things done.”

Why is it important to you to always be learning something new?

I like to keep learning to keep up with my profession. I’ve been a software developer for 20 years, and have had to redefine what I do a number of times. New technologies are being invented all the time, and not learning about them and taking advantage of them is foolish. The learning I’ve done definitely broadened my horizons considerably. And I hope it always makes my contribution to society more valuable.

–

Congratulations Antal, on successfully making the leap into machine learning and data science. Your new role sounds amazing and we’re certain you’re going to have a very positive impact!

Next Post

This is all lie.
Udacity’s “Nanodegree” is considered by most of the employers as “On-line course”.
You literally pay 1000$ for 3 months of access to the online course.
They say they will assign you a classroom mentor. In my case, they didn’t. Only when I reached their support after few weeks from start of the course, they did. Then my mentor disappeared because they “had a problems with a vendor, who provided the mentorship platform”. Like “we have a problem and you have to be patient and understand and so on”. But when I couldn’t hit the deadline because of their problems, they just blocked my access to all the content. I reached their support and they just threw their terms of service in my face. Like “it’s your problems and we don’t care”.
Also the content of the Nanodegrees is often copy-pasted from other Nanodegrees and free courses, so the learning material is often intermittent and incomplete.
Keep it in mind if you are in doubt, to pay or not to pay.

We appreciate that you shared your concerns with us, and we’re sorry for any frustration or disappointment you experienced. We do understand from our support team that your matter has been resolved satisfactorily, and we look forward to great learning experiences for you going forward!

No, they did nothing satisfactory with my request. And the question was not resolved. Otherwise I wouldn’t post it. As I said, they just copy-pasted the terms of use and that’s all. I asked for one additional week for free or with penalty payment, I do not have additional 1000$ to finish one last function in the last project. You guys had issues with the most useful service and I understood it. But you don’t want to understand studen’t issues.

We do everything we can to understand and respond to our student’s issues, and have been working to do the same with regards to the matter(s) you raised. We’ll continue to do so, and trust that a satisfactory result is achievable. We’re only able to do so much through the comment platform on our blog, so will rely on our service channels to further advance any additional solutions that need to be reached. Thank you.